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Data Driven Track Before Detect Using Artificial Neural Networks
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- Karlsson, Alexander (author)
- KTH,Teknisk informationsvetenskap,Product Unit Electronic Surveillance Business Area Surveillance, SAAB AB, Stockholm, Sweden
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- Jansson, Magnus, Professor (author)
- KTH,Teknisk informationsvetenskap
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- Hamalainen, Mikael (author)
- Product Unit Electronic Surveillance Business Area Surveillance, SAAB AB, Stockholm, Sweden
- (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- English.
- In: 2023 IEEE International Radar Conference, RADAR 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- We present two neural network solutions for data driven track before detect applications. The detected tracks may be used to estimate good initial states for traditional trackers such as Kalman filters. We evaluate the method on different scenarios with multiple targets, non-linear trajectories, and different signal to noise ratio (SNR) values. Depending on scenario, the presented method achieves 99% detection probability on Swerling 3 and 4 targets at 5 - 13 dB SNR, with 0.04 - 0.001 false tracks per frame. The presented method is compared to a theoretically optimal detector.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
Keyword
- detection association
- max channel
- neural networks
- radar
- track before detect
- weak target detection
Publication and Content Type
- ref (subject category)
- kon (subject category)
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